Introduction to scipy stats.gamma() in Python

The scipy.stats.gamma() function in Python is a part of the scipy.stats module. It represents a continuous gamma distribution. The gamma distribution is a two-parameter family of continuous probability distributions. It is frequently used to model skewed positive continuous data.

Syntax

The syntax for using scipy.stats.gamma() is as follows:

scipy.stats.gamma(a, loc=0, scale=1)

Parameters

• a: Shape parameter of the gamma distribution (a > 0). It controls the shape of the distribution and must be provided.
• loc: Optional parameter representing the location parameter (default is 0). It shifts the distribution along the x-axis.
• scale: Optional parameter representing the scale parameter (default is 1). It stretches or compresses the distribution.

Methods

The scipy.stats.gamma() function provides various methods that can be used to analyze the gamma distribution. Some of the commonly used methods are:

• pdf(x): Probability density function at x.
• cdf(x): Cumulative distribution function at x.
• rvs(size): Random variates from the gamma distribution.
• mean(): Mean of the gamma distribution.
• var(): Variance of the gamma distribution.
• fit(data): Estimate distribution parameters from data.

Example

Here is an example that demonstrates the usage of scipy.stats.gamma():

import scipy.stats as stats

# Define the shape parameter
a = 2

# Create a gamma distribution with shape parameter 'a'
gamma_dist = stats.gamma(a)

# Compute the probability density function at x=3
pdf = gamma_dist.pdf(3)

# Compute the cumulative distribution function at x=4
cdf = gamma_dist.cdf(4)

print("PDF at x=3:", pdf)
print("CDF at x=4:", cdf)

This example creates a gamma distribution with a shape parameter of 2. It then calculates the probability density function (PDF) at x=3 and the cumulative distribution function (CDF) at x=4.

Conclusion

In this introduction, we covered the basic usage of the scipy.stats.gamma() function in Python. You can explore more methods and properties of the gamma distribution using this function to analyze and model your data.